A Streaming Approach for Association Rule Analysis of Spanish Politics on Twitter.

The technological era in which we live has supposed an exponential rise in the quantity of data daily-generated in the Internet. Social networks and particularly Twitter has been one of the most disruptive factors in this era, allowing people to share easily opinions and ideas. Data generated in this social network is an example of streams, which are outlined by the challenges that arise from their particular features: continue, unlimited, high-speed arrivals, demand of fast reaction and with changes over time (known as concept drifts). The dynamism that characterizes this type of problem requires from a streaming analysis in order to perform an adequate treatment. In this situation, data stream mining appears as an emergent field of data science with specialized machine learning techniques according to the nature of streams. One of the most prominent tasks in this field is association stream mining, which focuses on the problem of dynamical extraction of interesting association rules from data features in a situation where it is not possible to assume an priori data structure and there is an evolution of these data features over the time. This paper aims to carry out a proof of concept focused on politics by studying a real collection of tweets related to the 2019 Spanish Investiture process. Thereby, Fuzzy-CSar-AFP algorithm has been applied in order to carry out an online analysis of association rules among a collection of terms of interest from our Twitter database.